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Brain-age defines a structural signature of outcomes after vagus nerve stimulation in children
Timur H Latypov1, Hrishikesh Suresh2, Flavia Venetucci Gouveia1
1Neurosciences and Mental Health, SickKids Research Institute, Toronto, Canada.
Insights
A novel brain-age gap metric (BrainAGEgap) in children with drug-resistant epilepsy (DRE) predicts vagus nerve stimulation (VNS) treatment success. Lower pre-surgical BrainAGEgap indicates a higher likelihood of positive VNS outcomes in pediatric DRE patients.
Area of Science:
- Neuroscience
- Medical Imaging
- Epilepsy Research
Background:
- Predicting outcomes for vagus nerve stimulation (VNS) in pediatric drug-resistant epilepsy (DRE) is challenging.
- Investigating structural brain differences may explain variability in VNS response among children.
Purpose of the Study:
- To develop and validate a pediatric brain-age model.
- To assess the relationship between brain-age gap (BrainAGEgap) and VNS treatment response in pediatric DRE.
Main Methods:
- Developed a pediatric brain-age model using MRI from 2,623 healthy individuals.
- Validated the model's accuracy and generalizability in independent pediatric cohorts.
- Applied the model to 126 children with DRE undergoing VNS treatment.
Main Results:
- The brain-age model achieved high accuracy (R²=0.93) and generalized well.
- Children with DRE showed significantly elevated BrainAGEgap compared to controls.
- Lower pre-surgical BrainAGEgap predicted a higher likelihood of clinical response to VNS (p=0.01).
Conclusions:
- BrainAGEgap serves as a robust biomarker for disease burden and treatment responsiveness in pediatric DRE.
- This metric may enhance prognostic assessment and clinical decision-making for neuromodulation in pediatric epilepsy.
Introduction:
Outcomes following vagus nerve stimulation (VNS) are difficult to predict prior to surgery in pediatric drug-resistant epilepsy (DRE). We investigated whether structural brain differences among children may explain variability in VNS response.
Methods:
A pediatric-specific brain-age model was developed using structural MRI from 2623 healthy individuals aged 1.9 to 90 years, capturing nonlinear neurodevelopmental trajectories. Model accuracy was evaluated using cross-validation and independent validation in a pediatric cohort. The model was then applied to a multicenter cohort of 126 children with DRE treated with VNS.
Results:
The brain-age model demonstrated high accuracy (R2 = 0.93; mean absolute error = 3.5 years) and generalized well to an independent pediatric cohort (mean absolute error = 2.16 years). Applied to the VNS cohort, it revealed significantly elevated brain-age gaps (BrainAGEgap) compared to age- and sex-matched controls (t = 6.27, p < 0.0001), indicating cumulative structural divergence from age-referenced norms. BrainAGEgap correlated with baseline seizure burden and postoperative changes in seizure frequency. Lower pre-surgical BrainAGEgap predicted a significantly higher likelihood of becoming a clinical responder to VNS (p = 0.01). Local age within the cingulate cortex, thalamus, nucleus accumbens, and prefrontal cortex contributed disproportionately to BrainAGEgap differences, aligning with VNS-related circuitry.
Conclusion:
Developmental structural differences can be summarized using a robust, single-metric biomarker that reflects both disease burden and treatment responsiveness. BrainAGEgap may support individualized prognostic assessment and improve clinical decision-making in pediatric DRE undergoing neuromodulation.
